Predictive Modelling for Incremental Cold Flow Forming: An integrated framework for fundamental understanding and process optimisation

点击翻译按钮获取中文摘要

Incremental cold flow forming (ICFF) is a metal forming process for the production of high-quality, rotationally-symmetric, hollow engineering components as widely utilised by the aerospace, automotive and oil & gas sectors. In ICFF, a cylindrical preform is attached to a rotating mandrel and axially-translating rollers apply compression to the outer surface. This leads to extrusion of the workpiece material via significant plastic deformation. As a result of the incremental process - rollers are in contact with a small area of the exterior surface of the workpiece at any one time - the extrusion of the material occurs with significantly lower force than required for conventional forming processes. ICFF is thus well suited to high-strength, hard-to-deform materials. The process is "cold" as a coolant is applied where contact occurs between the workpiece and the roller. The deformation occurs significantly below the material's recrystallisation temperature. As a result, cold work hardening occurs leading to increased strength, stiffness and hardness of the final product. A significant advantage of ICFF over conventional forging and deep drawing is the flexibility it gives engineers to design complex components of varying size. ICFF can result in considerable cost savings via improved yields, reduced production times and improved material properties, as compared to standard manufacturing routes. Furthermore, ICFF allows for rapid prototyping to support virtual product design, thereby reducing development cost and driving innovation.Despite the significant advantages that ICFF has over conventional methods, considerable challenges remain. These must be overcome prior to its widespread adoption. Foremost is the unsatisfactory repeatability and reliability of the process; it can be unstable and failure of the material can occur. Controlling the complex ICFF process is challenging. This is compounded by the large number of process parameters and the highly nonlinear nature of the deformation. Critically, there is currently no accurate and robust model to elucidate the fundamental physical mechanisms that occur during ICFF. Without such a model, the application of ICFF to new products and materials will require costly trial-and-error component-scale testing and remain an art as opposed to a science. The primary aim of this collaborative research proposal between the Advanced Forming Research Centre (AFRC) and the Glasgow Computational Engineering Centre (GCEC) is to develop an engineering design framework to model ICFF. Understanding the response of materials to the loading regime imposed by ICFF is a key component of the model development. To this end, we will undertake a detailed materials characterisation study at the AFRC. The loading on the workpiece will be measured using a highly-instrumented, research-dedicated ICFF machine. In addition, a materials characterisation procedure for ICFF will be developed that will allow industry to test new materials for ICFF thereby reducing the need for costly ICFF trials.The computational model will build upon and significantly extend the existing framework provided by MoFEM - a state-of-the-art, general purpose finite element library developed within the GCEC. The model will account for all key features of ICFF, including significant deformations, contact between rotating parts, thermal effects and residual stresses. The highly non-linear and coupled nature of these processes makes modelling challenging. The modular nature of MoFEM allows us to focus on designing new, efficient and robust numerical methods for ICFF rather than developing the core of the library. The ability of the model to accurately simulate a range of ICFF applications will be demonstrated using component scale testing conducted at the AFRC. Finally the predictive capabilities of the model will be assessed by numerically optimising the process parameters to achieve a desired net shape.